# jq Primer: Munging JSON Data

Thursday, December 24, 2015

In this post, I'd like to summarize why I prefer to store data in JSON rather than CSV or TSV files, and show how jq can be used to process JSON files just as effectively as traditional Unix tools (such as cut, sed, awk, grep, etc) can be used to process CSV and TSV files.

# Why JSON?

When working with and analyzing data, we often have to deal with the data in many formats, and must choose what format we would like to have the data in for easiest processing. In the Python and R ecosystems, where tools such as Pandas and data frames are common, comma-delimited files (csv) or tab-delimited files (tsv) are common.

However, CSV and TSV files suffer from a few problems:

• Not all languages make it trivial to read these files. It is rarely difficult, but CSV and TSV files do not follow and rigid specification, and so parsing libraries can sometimes make differnt choices about how to deal with quotes, spaces, and so on, so incompatibility between languages or applications (Excel, Google Sheets, Numbers, etc) can sometimes become an issue.
• CSV and TSV files are hard to understand without context. If I look at a file with a bunch of columns, some of which are numeric, I will not know what each column is, unless there is a header. If there a header, parsing becomes trickier, because you must account for the header.
• CSV and TSV files enforce a very rigid format (a table of values). Not all data fits those formats; more importantly, while data may initially start out following that format, as the data source or generation procedure changes, it may not retain that format. For example, if a dataset originally has one type of row, and then another type of row is added with a few different columns, the columns must be added for all values, and must be blank for many of them.

None of these problems are showstoppers, and all of them are fairly easy to work around. However, instead of working around problems caused by an inflexible and fairly old format, we can can use an alternative format.

## JSON

JSON stands for JavaScript Object Notation, and is a text-based format which can encode numbers, booleans, arrays, and string-keyed dictionaries (objects).

• Unlike CSV, there exists a specification of JSON, and libraries for parsing JSON are as a result often slightly simpler to import and use than libraries for CSV files.
• JSON allows for a variety of rich structure and hierarchy in your data representation, so your data representation can grow with the complexity of your data.
• JSON requires less context and is more type-safe, since each dictionary allows access to its fields only through the field names, and not through an index, and the field names are immediately visible next to the data.

# jq Primer

A common complaint about JSON as a data exchange format is that it is harder to work with in an ad-hoc manner using command-line tools. In the Unix world, we have access to a large suite of tools for processing data:

• wc for counting.
• grep for searching.
• sed for editing by pattern matching with a regular expression.
• cat for combining files.
• cut for selecting fields from tabular formats.
• sort for sorting data.
• uniq for removing duplicates.
• head for selecting the beginning of the data.
• tail for selecting the end of the data.

All of these can be combined with pipes and fed a variety of options to produce very expressive and powerful computational pipelines on the fly, and they work mostly on raw and delimited text files, not JSON.

The jq tool fixes this. jq is a command-line tool for processing JSON in every imaginable way; its website describes it as the sed of the JSON world. In the remainder of this post, I'd like to give a bunch of uses of standard command-line utilities and show how then can easily be replicated with jq.

All code in this article applies to jq version 1.5, the latest version as of this writing.

# Unix vs jq

In this section, I will directly compare standard Unix commands with their corresponding jq commands. I will not include any test input or output data; if you would like to test these commands with something, you can use these two files as starting points:

data.tsv:

Andrew,31,Honda,2015,California
Jane,27,Ford,2002,Maryland
Igor,55,Toyota,1998,Kansas
Ann,15,,,Kansas

data.json:

{ "name": "Andrew", "age": 31, "car_type": "Honda", "car_make": 2015, "state": "California" }
{ "name": "Jane", "age": 27, "car_type": "Ford", "car_make": 2002, "state": "Maryland" }
{ "name": "Igor", "age": 55, "car_type": "Toyota", "car_make": 1998, "state": "Alaska" }
{ "name": "Igor", "age": 55, "car_type": "Toyota", "car_make": 1998, "state": "Kansas" }
{ "name": "Igor", "age": 55, "car_type": "Toyota", "car_make": 1998, "state": "Alaska" }
{ "name": "Ann", "age": 15, "car_type": null, "car_make": null, "state": "Kansas" }

# cat

cat is used for printing data to the screen and combining multiple files.

### Outputting data:

# Bash
$cat data.tsv # jq$ jq . data.json

jq takes a filter as its first argument, which describes the transformation to do to the JSON. The filter . is the empty filter, which just outputs the data directly.

This jq command outputs:

{
"name": "Andrew",
"age": 31,
"car_type": "Honda",
"car_make": 2015,
"state": "California"
}
{
"name": "Jane",
"age": 27,
"car_type": "Ford",
"car_make": 2002,
"state": "Maryland"
}
...

### Combining files

# Bash
$cat data.tsv data.tsv # jq$ jq . data.json data.json

jq takes as many files as you need to read, just like cat.

### jq Options

Since JSON is a more complex format, you have more options on how to output it. You can use jq -c to output to a single line and jq -r to not put quotes around outputted strings.

# cut

cut lets you select fields from a tab-delimited file, with options to use any character as a delimiter.

### Selecting One Field

To select the first field, we would use:

# Bash
$cut -f1 # jq$ jq '.field1'

When using jq, we have to provide the name of the field in the JSON objects, rather than the index of the field in the columns.

### Selecting Many Fields

To select the first field and third field, we would use:

# Bash
$cut -f1,3 # jq$ jq '{field1, field2}'

In jq, this will create objects with field1 and field2 as fields, but nothing else. If you would like to create arrays instead, you can use:

# jq with arrays

# jq

# jq

# jq
$jq -s '.[10:20]' In bash, using the +N value for tail -n or a -N value for head -n will display everything except those elements; thus, tail -n +11 will display everything but the first ten elements. In jq, we use the same consistent and simple array slice notation to select our range of indices. # wc To count the number of values in a file, we could use: # Bash$ wc -l

# jq

# jq

# jq
$jq -s 'sort_by(.field2) Any filter could be used inside the sort_by, which makes jq much more powerful than sort, as the sort order can be a complex bit of logic and not just a field. For example, you can easily sort a list of arrays by their average value, whereas that would require writing code with a tab-delimited file. # uniq and sort -u To remove duplicates, we can use the uniq tool, or the -u option to sort. ### Getting Rid of Duplicates # Bash (option 1)$ sort | uniq

# Bash (option 2)
$sort -u # jq (option 1)$ jq -s unique

# jq (option 1, unique on a field)
$jq -s 'unique_by(.field1)' These examples show different ways of removing duplicates. The bash examples are identical. With jq, we can choose what we could as a duplicate, and we can remove duplicates based on only some fields or based on some function of the fields. For example, from a list of arrays, we could find one array of every length by using jq -s 'unique_by(length)'. ### Counting Number of Repeats To count the number of repeats of values in a file, we could use: # Bash$ sort | uniq -c

# jq
$jq -s 'group_by(.) | map({(.[0]): length}) | add' In this case, it's pretty clear that the bash representation is shorter and easier; however, it's impressive that we can build uniq -c out of more basic tools provided to us by jq, and shows us how powerful it can be when we combine these tools. We use a few new bits in the above command. First of all, we use jq pipes (the | operator). Pipes will take the output of the filter on the left and pipe it as input to the filter on the right, just like in bash; however, instead of the data being text, the data is JSON objects. This composite filter has three pieces, each one piped into the next: 1. group_by(.): This filter takes an array and separates it into buckets, where each bucket contains the same value. 2. map({(.[0]): length}): This filter is a gnarly beast. map is a filter that applies a filter to each element of an array. The filter map applies is {(.[0]): length}. This filter constructs an object where the key is obtained from the .[0] filter and the value is obtained from the length filter; in other words, given an input that is an array, the key is the first element of the array and the value is the length of the array. Taken all together, this transforms something like [["a", "a"], ["b", "b", "b", "b"], ["c"]] into [{"a": 2}, {"b": 4}, {"c": 1}]. 3. add: The filter add adds an array of things together; in the case of numbers, it is numeric addition; in the case of objects, it merges them together and creates a new object whose keys are the union of the keys of the objects being merged. Altogether, we group our input array by the value, then turn the groups into {value: count} dictionaries, and then merge those dictionaries together. Here is an example: $ echo '["a", "b", "a"]' | jq -c 'group_by(.)'
[["a","a"], ["b"]]

$echo '["a", "b", "a"]' | jq -c 'group_by(.) | map({(.[0]): length})' [{"a":2}, {"b":1}]$ echo '["a", "b", "a"]' | jq -c 'group_by(.) | map({(.[0]): length}) | add'
{"a":2, "b":1}

# grep

grep lets us search through our data for strings or regular expressions.

### Searching for a String

To search for the string string in our data, we could use:

# Bash
$grep string # jq$ jq 'select(.key == "string")'

When using jq, we have to choose what key we'd like to check. The select filter applies a filter to every element, and then only keeps elements for which the filter is true.

### Searching for a Regex

To search for the strings string, bother, or mother in our data, we could use:

# Bash
$grep -E '(string|[bm]other)' # jq$ jq 'select(test("(string|[bm]other)"))'

We use the test filter, which applies a regex to a string and returns true if it matched.

# sed

sed lets us apply regex search and replace to every line of a file. To do this, we could do something like this, which replaces capital letters with dots:

# Bash
$sed 's/[A-Z]/./g # jq$ jq 'gsub("[A-Z]"; ".")'

We use the gsub filter, which applies a regex search and replace as many times as it can. To only do the search and replace once we could have used sub instead.

# awk

awk is the Swiss army knife of Unix text processing. It is an entire programming languagee built for quick and dirty text manipulation, just like jq is built for JSON manipulation. As a result, they can do quite similar things.

### Filter a File

To select only elements where the third column is greater than ten, we could use:

# Bash
$awk '$3 >= 10'

# jq
$jq 'select(.field3 >= 10)' ### Sum a Field To sum all the first columns of a file, we could use: # Bash$ awk '{ sum += $1 } END { print sum }' # jq$ jq -s 'map(.field1) | add'

We use the add filter for jq, which can sum a list of numbers.

### Average a Field

To average all the first columns of a file, we could use:

# Bash
$awk '{ sum +=$1; n += 1; } END { print (sum / n) }'

# jq
\$ jq -s 'add / length'

Our jq filter applies the add and the length filters and divides the result of add by the result of length.

# Conclusion

The commands above barely scratch the surface of what jq can do. jq supports variables, functions, modules, control over how it reads its input, streaming huge (many gigabyte-sized) JSON files while processing them, math, recursion, and a variety of other things, including plenty more built-in functions than the ones we looked at. Although there is certainly a learning curve, it is an incredibly productive and effective tool once you achieve a basic fluency init.

For me, this makes working with JSON much easier than working with similar tab or comma delimited files. In addition, jq has native support for reading and writing tab-delimited files, so it is easy to convert between TSV and JSON at will.

Finally, while I portrayed jq as an alternative to the standard Unix toolset, it is really more of a complement. In my own daily usage, I regularly combine jq -c with head and tail (when I need subsets of records), grep (when I am searching for particular strings, but don't know what keys to look in), sed (when I have some string substitution to make across many keys), and so on. Always use the best tool for the job!

For more information, I recommend reading the jq manual; it is extensive and well-written.

Happy jq` hacking!